Big data, machine learning, and population health: predicting cognitive outcomes in childhood

Predictors of early child development for screening pregnant women most in need of support in Brazil

Background

Home visiting programmes can support child development and reduce inequalities, but failure to identify the most vulnerable families can undermine such efforts. We examined whether there are strong predictors of poor child development that could be used to screen pregnant women in primary health care settings to target early interventions in a Brazilian population. Considering selected predictors, we assessed coverage and focus of a large-scale home visiting programme named Primeira Infância Melhor (PIM).

Methods

We undertook a prospective cohort study on 3603 children whom we followed from gestation to age four years. We then used 27 potential socioeconomic, psychosocial, and clinical risk factors measurable during pregnancy to predict child development, which was assessed by the Battelle Developmental Inventory (BDI) at the age of four years. We compared the results from a Bonferroni-adjusted conditional inference tree with exploratory linear regression and principal component analysis (PCA), and we conducted external validation using data from a second cohort from the same population. Lastly, we assessed PIM coverage and focus by linking 2015 cohort data with PIM databases.

Results

The decision tree analyses identified maternal schooling as the most important variable for predicting BDI, followed by paternal schooling. Based on these variables, a group of 214 children who had the lowest mean BDI (BDI = -0.48; 95% confidence interval (CI) = -0.63, -0.33) was defined by mothers with ≤5 years and fathers with ≤4 years of schooling. Maternal and paternal schooling were also the strongest predictors in the exploratory analysis using regression and PCA, showing linear associations with the outcome. However, their capacity to explain outcome variance was low, with an adjusted R2 of 5.3% and an area under the receiver operating characteristic curve of 0.62 (95% CI = 0.60, 0.64). External validation showed consistent results. We also provided an online screening tool using parental schooling data to support programme's targeting. PIM coverage during pregnancy was low, but the focus was adequate, especially among families with longer enrolment, indicating families most in need received higher dosage.

Conclusions

Information on maternal and paternal schooling can improve the focus of home visiting programmes if used for initial population-level screening of pregnant women in Brazil. However, enrolment decisions require complementary information on parental resources and direct interactions with families to jointly decide on inclusion.

Predicting low cognitive ability at age 5 years using perinatal data and machine learning

BACKGROUND

There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for this purpose.

METHODS

Data were from 8858 participants in the Growing Up in Ireland cohort, a nationally representative study of infants and their primary caregivers (PCGs). Maternal, infant, and socioeconomic characteristics were collected at 9-months and cognitive ability measured at age 5 years. Data preprocessing, synthetic minority oversampling, and feature selection were performed prior to training a variety of machine learning models using ten-fold cross validated grid search to tune hyperparameters. Final models were tested on an unseen test set.

RESULTS

A random forest (RF) model containing 15 participant-reported features in the first year of infant life, achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 for predicting low cognitive ability at age 5. This model could detect 72% of infants with low cognitive ability, with a specificity of 66%.

CONCLUSIONS

Model performance would need to be improved before consideration as a population-level screening tool. However, this is a first step towards early, individual, risk stratification to allow targeted childhood screening.

IMPACT

This study is among the first to investigate whether machine learning methods can be used at a population-level to predict which infants are at high risk of low cognitive ability in childhood. A random forest model using 15 features which could be easily collected in the perinatal period achieved an AUROC of 0.77 for predicting low cognitive ability. Improved predictive performance would be required to implement this model at a population level but this may be a first step towards early, individual, risk stratification.

The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet

Introduction

Neurodevelopment and related mental disorders (NDDs) are one of the most frequent disabilities among young people. They have complex clinical phenotypes often associated with transnosographic dimensions, such as emotion dysregulation and executive dysfunction, that lead to adverse impacts in personal, social, academic, and occupational functioning. Strong overlap exists then across NDDs phenotypes that are challenging for diagnosis and therapeutic intervention. Recently, digital epidemiology uses the rapidly growing data streams from various devices to advance our understanding of health's and disorders' dynamics, both in individuals and the general population, once coupled with computational science. An alternative transdiagnostic approach using digital epidemiology may thus better help understanding brain functioning and hereby NDDs in the general population.

Objective

The EPIDIA4Kids study aims to propose and evaluate in children, a new transdiagnostic approach for brain functioning examination, combining AI-based multimodality biometry and clinical e-assessments on an unmodified tablet. We will examine this digital epidemiology approach in an ecological context through data-driven methods to characterize cognition, emotion, and behavior, and ultimately the potential of transdiagnostic models of NDDs for children in real-life practice.

Methods and analysis

The EPIDIA4Kids is an uncontrolled open-label study. 786 participants will be recruited and enrolled if eligible: they are (1) aged 7 to 12 years and (2) are French speaker/reader; (3) have no severe intellectual deficiencies. Legal representative and children will complete online demographic, psychosocial and health assessments. During the same visit, children will perform additionally a paper/pencil neuro-assessments followed by a 30-min gamified assessment on a touch-screen tablet. Multi-stream data including questionnaires, video, audio, digit-tracking, will be collected, and the resulting multimodality biometrics will be generated using machine- and deep-learning algorithms. The trial will start in March 2023 and is expected to end by December 2024.

Discussion

We hypothesize that the biometrics and digital biomarkers will be capable of detecting early onset symptoms of neurodevelopment compared to paper-based screening while as or more accessible in real-life practice.

Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments

Introduction

Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development.

Methods

This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age.

Results

This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age.

Discussion

The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.